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. 2017 Nov 10;7:15240. doi: 10.1038/s41598-017-15695-w

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© The Author(s) 2017

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Evaluating the performance of different clustering algorithms. (a) Performance of DBSCAN (DB) on a modeled clustered distribution (cluster density: 30 μm⁻²) with different localization point densities. Dashed grey circles demarcate the areas within which the localizations points were randomly distributed. Localization points are color-coded based on their cluster assignment (grey points represent noise). (b) Adjusted Rand scores (ARS) computed from 20 different SDPs. (c–h) Same as in a and b, but for the affinity propagation (AP, c,d), mean shift (MS, e, f), and Bayesian clustering (BC, g,h) algorithms. Open black symbols correspond to individual SDPs (n = 20), solid black symbols are the ARS values of the examples shown on the left, red symbols represent mean ± SD.